The manufacture of products or other items commonly involves a multi-stage process that includes the use of equipment of various capacities. In such a multi-stage, variable equipment size process, product or end-item demands are often aggregated or split into manufacturing batches in order to fit the available equipment sizes. The scheduling of these batches must account for the complex factory flows between the manufacturing stages and as well as various business rules unique to the particular industry involved. If the manufacturing process is used to produce multiple products, the scheduling process also preferably minimizes sequence-dependent equipment changeovers between the scheduled batches.
Computer implemented planning and scheduling systems are often used for manufacturing and other supply chain planning functions. In general, such systems can model the manufacturing and related environments and provide plans or schedules for producing items to fulfill consumer demand within the constraints of the environment. Existing scheduling systems, however, typically cannot handle variable equipment sizes or make optimal batching decisions using a number of different criteria. Often a manual heuristic scheme is used, based on the personal expertise of a human operator, to divide demand for a product into batches of a single size and to schedule the batches. However, these heuristic schemes often lead to unsatisfactory factory schedules in terms of under-utilized resources, late deliveries, excess inventories, and overall unbalanced factories. Moreover, they necessarily require a person with detailed knowledge of and extensive experience with the manufacturing process for which the batch aggregation and scheduling is required. These and other deficiencies make previous systems and methods for aggregating and scheduling batches inadequate for many purposes.